simu_tbss: Simulation function for TBSS algorithm

In VARDetect: Multiple Change Point Detection in Structural VAR Models

Simulation function for TBSS algorithm

Description

Function for deploying simulation using TBSS algorithm

Usage

simu_tbss(
  nreps,
  simu_method = c("sparse", "group sparse", "fLS"),
  nob,
  k,
  lags = 1,
  lags_vector = NULL,
  brk,
  sigma,
  skip = 50,
  group_mats = NULL,
  group_type = c("columnwise", "rowwise"),
  group_index = NULL,
  sparse_mats = NULL,
  sp_density = NULL,
  signals = NULL,
  rank = NULL,
  info_ratio = NULL,
  sp_pattern = c("off-diagonal", "diagoanl", "random"),
  singular_vals = NULL,
  spectral_radius = 0.9,
  est_method = c("sparse", "group sparse", "fLS"),
  q = 1,
  tol = 0.01,
  lambda.1.cv = NULL,
  lambda.2.cv = NULL,
  mu = NULL,
  group.index = NULL,
  group.case = c("columnwise", "rowwise"),
  max.iteration = 100,
  refit = FALSE,
  block.size = NULL,
  blocks = NULL,
  use.BIC = TRUE,
  an.grid = NULL
)

Arguments

nreps	A numeric integer number, indicates the number of simulation replications
simu_method	the structure of time series: "sparse","group sparse", and "fLS"
nob	sample size
k	dimension of transition matrix
lags	lags of VAR time series. Default is 1.
lags_vector	a vector of lags of VAR time series for each segment
brk	a vector of break points with (nob+1) as the last element
sigma	the variance matrix for error term
skip	an argument to control the leading data points to obtain a stationary time series
group_mats	transition matrix for group sparse case
group_type	type for group lasso: "columnwise", "rowwise". Default is "columnwise".
group_index	group index for group lasso.
sparse_mats	transition matrix for sparse case
sp_density	if we choose random pattern, we should provide the sparsity density for each segment
signals	manually setting signal for each segment (including sign)
rank	if we choose method is low rank plus sparse, we need to provide the ranks for each segment
info_ratio	the information ratio leverages the signal strength from low rank and sparse components
sp_pattern	a choice of the pattern of sparse component: diagonal, 1-off diagonal, random, custom
singular_vals	singular values for the low rank components
spectral_radius	to ensure the time series is piecewise stationary.
est_method	method: sparse, group sparse, and fixed low rank plus sparse. Default is sparse
q	the AR order
tol	tolerance for the fused lasso
lambda.1.cv	tuning parameter lambda_1 for fused lasso
lambda.2.cv	tuning parameter lambda_2 for fused lasso
mu	tuning parameter for low rank component, only available when method is set to "fLS"
group.index	group index for group sparse case
group.case	group sparse pattern: column, row.
max.iteration	max number of iteration for the fused lasso
refit	logical; if TRUE, refit the VAR model for parameter estimation. Default is FALSE.
block.size	the block size
blocks	the blocks
use.BIC	use BIC for k-means part
an.grid	a vector of an for grid searching

Value

A S3 object of class, named VARDetect.simu.result

est_cps

A list of estimated change points, including all replications

est_sparse_mats

A list of estimated sparse components for all replications

est_lowrank_mats

A list of estimated low rank components for all replications

est_phi_mats

A list of estimated model parameters, transition matrices for VAR model

running_times

A numeric vector, containing all running times

Examples

nob <- 4000; p <- 15
brk <- c(floor(nob / 3), floor(2 * nob / 3), nob + 1)
m <- length(brk); q.t <- 1
sp_density <- rep(0.05, m * q.t)
signals <- c(-0.6, 0.6, -0.6)
try_simu <- simu_tbss(nreps = 3, simu_method = "sparse", nob = nob, 
                      k = p, lags = q.t, brk = brk, sigma = diag(p), 
                      signals = signals, sp_density = sp_density, 
                      sp_pattern = "random", est_method = "sparse", q = q.t, 
                      refit = TRUE)

VARDetect documentation built on May 10, 2022, 9:07 a.m.